Traditional syntactic searches for expressions within corpus of terms suffer from a range of problems. For instance, homonymic terms are used in different meanings both within a corpus of terms being searched and by the searching agent. Consequently, many irrelevant results are returned which can be sorted out only by human inspection. The searching agent may use a synonym of terms contained in the corpus, thus not all relevant results are returned. The search agent may use hyponyms or hypernyms of terms contained in a corpus. So even in cases where no precise syntactic match is available in the corpus, some related terms may still be useful but will not be found due to rigidity of the current methods. One way to address some of these problems is if the search agents target the semantics of terms within a corpus instead of purely relying on syntactic similarities.
The search criteria and their target artifacts however are not always simple individual terms. Instead, both multi-term search expressions and multi-term target expressions in the corpus of terms are common. Determining semantic similarity between multi-term search expressions and multi-term target expressions in orders of magnitude is more complicated than working with single term expressions.
Thus there is a need for methods and systems that simplifies the retrieval of multi-term result expressions from a corpus of target multi-term expressions terms based on a quantified semantic similarity between the multi-term search expression and the multi-term target expressions in the corpus.